Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.Author summary: Timely forecasts of infectious disease transmission can help public health officials, health care providers, and individuals better prepare for and respond to disease outbreaks. Work in recent years has led to the development of a number of forecast systems. These systems provide important information on future disease incidence; however, all forecasting systems contain inaccuracies, or error. This error can be reduced by combining information from multiple forecasting systems into a superensemble using Bayesian averaging methods. Here we compare 21 forecasting systems for seasonal influenza outbreaks and use them together to create superensemble forecasts. The superensemble produces more accurate forecasts than the individual systems, improving our ability to predict the timing and severity of seasonal influenza outbreaks.